@inproceedings{kolhatkar-taboada-2017-constructive,
    title = "Constructive Language in News Comments",
    author = "Kolhatkar, Varada  and
      Taboada, Maite",
    editor = "Waseem, Zeerak  and
      Chung, Wendy Hui Kyong  and
      Hovy, Dirk  and
      Tetreault, Joel",
    booktitle = "Proceedings of the First Workshop on Abusive Language Online",
    month = aug,
    year = "2017",
    address = "Vancouver, BC, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/W17-3002/",
    doi = "10.18653/v1/W17-3002",
    pages = "11--17",
    abstract = "We discuss the characteristics of constructive news comments, and present methods to identify them. First, we define the notion of constructiveness. Second, we annotate a corpus for constructiveness. Third, we explore whether available argumentation corpora can be useful to identify constructiveness in news comments. Our model trained on argumentation corpora achieves a top accuracy of 72.59{\%} (baseline=49.44{\%}) on our crowd-annotated test data. Finally, we examine the relation between constructiveness and toxicity. In our crowd-annotated data, 21.42{\%} of the non-constructive comments and 17.89{\%} of the constructive comments are toxic, suggesting that non-constructive comments are not much more toxic than constructive comments."
}